yolov7自动停止（设置patience）且输出最优模型时的PR图（test best.py）
步骤1：在utils文件夹下的torch_utils.py里添加如下class
class EarlyStopping:
# YOLOv5 simple early stopper
def __init__(self, patience=30):
self.best_fitness = 0.0 # i.e. mAP
self.best_epoch = 0
self.patience = patience or float('inf') # epochs to wait after fitness stops improving to stop
self.possible_stop = False # possible stop may occur next epoch
def __call__(self, epoch, fitness):
if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training
self.best_epoch = epoch
self.best_fitness = fitness
delta = epoch - self.best_epoch # epochs without improvement
self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch
stop = delta >= self.patience # stop training if patience exceeded
if stop:
logger.info(f'Stopping training early as no improvement observed in last {self.patience} epochs. '
f'Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n'
f'To update EarlyStopping(patience={self.patience}) pass a new patience value, '
f'i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping.')
return stop步骤2：在train.py前面添加模块
from utils.torch_utils import EarlyStopping步骤3：在train.py文件里大概第三百行start training中scaler变量下面添加如下两行
stopper: EarlyStopping
stopper, stop = EarlyStopping(patience=opt.patience), False步骤4：在train.py文件里大概450行的位置，#Save model上方加入如下代码（并将原来的#Update best mAP部分删除）
# Update best mAP
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
stop = stopper(epoch=epoch, fitness=fi) # early stop check
if fi > best_fitness:
best_fitness = fi
wandb_logger.end_epoch(best_result=best_fitness == fi)步骤5：在train.py文件里大概490行的位置，#end epoch上方加入如下代码
# EarlyStopping
if rank != -1: # if DDP training
broadcast_list = [stop if rank == 0 else None]
dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks
if rank != 0:
stop = broadcast_list[0]
if stop:
break # must break all DDP ranks
步骤6：在train.py文件里大概580行的位置，加入一行
parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
步骤7：在train.py文件里大概425行的位置，将原先# Calculate mAP的部分更改为
if not opt.notest or final_epoch: # Calculate mAP
wandb_logger.current_epoch = epoch + 1
results, maps, times = test.test(data_dict,
batch_size=batch_size * 2,
imgsz=imgsz_test,
model=ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
verbose=nc < 50 ,
plots=False,
wandb_logger=wandb_logger,
compute_loss=compute_loss,
is_coco=is_coco)步骤8：在train.py文件里大概500行的位置，将原先# Test best.pt的部分更改为
# Test best.pt
logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
results, _, _ = test.test(opt.data,
batch_size=batch_size * 2,
imgsz=imgsz_test,
conf_thres=0.001,
iou_thres=0.7,
model=attempt_load(best, device).half(),
single_cls=opt.single_cls,
dataloader=testloader,
verbose=nc < 50,
save_dir=save_dir,
save_json=True,
plots=plots,
wandb_logger=wandb_logger,
compute_loss=compute_loss,
is_coco=is_coco)步骤9：在test.py（注意换文件啦！）里大概293行的地方将iou-thres的默认值更改为0.7
parser.add_argument('--iou-thres', type=float, default=0.7, help='IOU threshold for NMS')在第26行的地方将iou-thres的值更改为0.7
iou_thres=0.7, # for NMS~~~~~~~~~~~然后正常运行就可以啦~~~~~~~~完结*★,°*:.☆(￣▽￣)/$:*.°★*~~~~~~~~~~~~~